A New FP-Tree Algorithm for Mining Frequent Itemsets

摘要

Data mining has become an important field and has been applied extensively across many areas. Mining frequent itemsets in a transaction database is critical for mining association rules. Many investigations have established that pattern-growth method outperforms the method of Apriori-like candidate generation. The performance of the pattern-growth method depends on the number of tree nodes. Accordingly, this work presents a new FP-tree structure (NFP-tree) and develops an efficient approach for mining frequent itemsets, based on an NFP-tree, called the NFP-growth approach. NFP-tree employs two counters in a tree node to reduce the number of tree nodes. Additionally, the header table of the NFP-tree is smaller than that of the FP-tree. Therefore, the total number of nodes of all conditional trees can be reduced. Simulation results reveal that the NFP-growdi algorithm is superior to the FP-growth algorithm for dense datasets and real datasets.